
We show current state-of-the-art (SOTA) neural entity linking models based on attention-based bag-of-words context model often produce type errors and analyze the possible causes.

We propose a novel entity embedding method based on pre-trained BERT to better capture latent entity type information.

We integrate a BERT-based entity similarity into the local model of a SOTA model [1]}.

We verify the effectiveness of our model on standard benchmark datasets and achieve significant improvement over the baseline. And the detailed experiment analysis demonstrates that our method truly corrects most of the type errors produced by the baseline.

